Integrated optimization of train services plan and passenger flow control on an oversaturated suburban metro line

Abstract

Suburban metro lines play an important role in daily commuting, which transport passengers from suburban areas to the city center in the morning peak. However, the excessive commuting demand leads to oversaturation on trains and platforms, especially at the stations near the city center. To ease the oversaturation of suburban metro lines, an integrated optimization on inbound passenger control and train service planning including short-turn and non-periodic services has been proposed. The proposed approach aims to reduce the maximum number of stranded passengers at the platforms while the total number of waiting passengers outside stations and the running kilometers of all train services are also minimized. To ensure the feasibility of solutions, the train connections and available rolling stock fleet size have been formulated. A genetic algorithm embedded with brute force is adopted to efficiently solve the proposed large-scale nonlinear model. Finally, a real case of Beijing subway Fangshan line shows that the inbound passenger control and short-turn services help to evidently reduce the stranded passengers at the stations near the city center. Compared with the practical train services without inbound passenger control and the integrated model without short-turn services, the integrated model is able to improve the sum of three objectives by more than 14.8%.

Keywords

suburban metro line / short-turn train service / inbound passenger control / train scheduling / passenger fairness

References

[1] ShenQ, ChenP, PanH. Factors affecting car ownership and mode choice in rail transit-supported suburbs of a large Chinese city [J]. Transportation Research Part A: Policy and Practice, 2016, 94: 31-44

[2] YangJ, JinJ, WuJ, et al. . Optimizing passenger flow control and bus-bridging service for commuting metro lines [J]. Computer-Aided Civil and Infrastructure Engineering, 2017, 32(6): 458-473

[3] JiangZ, FanW, LiuW, et al. . Reinforcement learning approach for coordinated passenger inflow control of urban rail transit in peak hours [J]. Transportation Research Part C: Emerging Technologies, 2018, 88: 1-16

[4] ChenZ, GanJ, WangX. Three-level passenger flow control model for urban rail transit in consideration of safety [J]. Journal of Central South University (Science and Technology), 2020, 51(5): 1441-1450(in Chinese)

[5] YaoX, ZhaoP, QiaoK, et al. . Modeling on coordinated passenger inflow control for urban rail transit network [J]. Journal of Central South University (Science and Technology), 2015, 46(1): 342-350(in Chinese)

[6] XuX, LiH, LiuJ, et al. . Passenger flow control with multi-station coordination in subway networks: Algorithm development and real-world case study [J]. Transportmetrica B: Transport Dynamics, 2019, 7(1): 446-472

[7] ZouQ, YaoX, ZhaoP, et al. . Managing recurrent congestion of subway network in peak hours with station inflow control [J]. Journal of Advanced Transportation, 2018, 2018: 1-16

[8] YangH, QiaoF. An equity-based passenger flow control model with application to Hong Kong-Shenzhen border-crossing [J]. Journal of Advanced Transportation, 2002, 36(2): 169-185

[9] ShangP, LiR, LiuZ, et al. . Equity-oriented skip-stopping schedule optimization in an oversaturated urban rail transit network [J]. Transportation Research Part C: Emerging Technologies, 2018, 89: 321-343

[10] LiS, XuR, HanK. Demand-oriented train services optimization for a congested urban rail line: Integrating short turning and heterogeneous headways [J]. Transportmetrica A Transport Science, 2019, 15(2): 1459-1486

[11] CortésC E, Jara-DíazS, TirachiniA. Integrating short turning and deadheading in the optimization of transit services [J]. Transportation Research Part A: Policy and Practice, 2011, 45(5): 419-434

[12] NiuH, ZhouX. Optimizing urban rail timetable under time-dependent demand and oversaturated conditions [J]. Transportation Research Part C: Emerging Technologies, 2013, 36: 212-230

[13] SunL, JinJ, LeeD H, et al. . Demand-driven timetable design for metro services [J]. Transportation Research Part C: Emerging Technologies, 2014, 46: 284-299

[14] BarrenaE, CancaD, CoelhoL C, et al. . Exact formulations and algorithm for the train timetabling problem with dynamic demand [J]. Computers & Operations Research, 2014, 44: 66-74

[15] BaiY, HuQ, HoT K, et al. . Timetable optimization for metro lines connecting to intercity railway stations to minimize passenger waiting time [J]. IEEE Transactions on Intelligent Transportation Systems, 2021, 22(1): 79-90

[16] WangY, TangT, NingB, et al. . Integrated optimization of regular train schedule and train circulation plan for urban rail transit lines [J]. Transportation Research Part E: Logistics and Transportation Review, 2017, 105: 83-104

[17] YueY, HanJ, WangS, et al. . Integrated train timetabling and rolling stock scheduling model based on time-dependent demand for urban rail transit [J]. Computer-Aided Civil and Infrastructure Engineering, 2017, 32(10): 856-873

[18] ShiJ, YangL, YangJ, et al. . Service-oriented train timetabling with collaborative passenger flow control on an oversaturated metro line: An integer linear optimization approach [J]. Transportation Research Part B: Methodological, 2018, 110: 26-59

[19] LiS, DessoukyM M, YangL, et al. . Joint optimal train regulation and passenger flow control strategy for high-frequency metro lines [J]. Transportation Research Part B: Methodological, 2017, 99: 113-137

[20] LiuR, LiS, YangL. Collaborative optimization for metro train scheduling and train connections combined with passenger flow control strategy [J]. Omega, 2020, 90101990

[21] JiangM, LiH, XuX, et al. . Metro passenger flow control with station-to-station cooperation based on stop-skipping and boarding limiting [J]. Journal of Central South University, 2017, 24(1): 236-244

[22] ChenY, AnK. Integrated optimization of bus bridging routes and timetables for rail disruptions [J]. European Journal of Operational Research, 2021, 295(2): 484-498

[23] ZhaoN, RobertsC, HillmansenS. The application of an enhanced Brute Force algorithm to minimise energy costs and train delays for differing railway train control systems [J]. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, 2014, 228(2): 158-168